TY - GEN
T1 - A convex approach to steady state moment analysis for stochastic chemical reactions
AU - Sakurai, Yuta
AU - Hori, Yutaka
N1 - Funding Information:
This work was supported in part by JSPS KAKENHI Grant Number JP16H07175, Okawa Foundation Research Grant under grant number 16-10, Keio Gijuku Academic Development Funds and Research Grant of Keio Leading-edge Laboratory of Science and Technology Y. Sakurai and Y. Hori are with Department of Applied Physics and Physico-Informatics, Keio University, Japan. y.sakurai-5861@keio.jp, yhori@appi.keio.ac.jp
Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/18
Y1 - 2018/1/18
N2 - Model-based prediction of stochastic noise in biomolecular reactions often resorts to approximation with unknown precision. As a result, unexpected stochastic fluctuation causes a headache for the designers of biomolecular circuits. This paper proposes a convex optimization approach to quantifying the steady state moments of molecular copy counts with theoretical rigor. We show that the stochastic moments lie in a convex semi-algebraic set specified by linear matrix inequalities. Thus, the upper and the lower bounds of some moments can be computed by a semidefinite program. Using a protein dimerization process as an example, we demonstrate that the proposed method can precisely predict the mean and the variance of the copy number of the monomer protein.
AB - Model-based prediction of stochastic noise in biomolecular reactions often resorts to approximation with unknown precision. As a result, unexpected stochastic fluctuation causes a headache for the designers of biomolecular circuits. This paper proposes a convex optimization approach to quantifying the steady state moments of molecular copy counts with theoretical rigor. We show that the stochastic moments lie in a convex semi-algebraic set specified by linear matrix inequalities. Thus, the upper and the lower bounds of some moments can be computed by a semidefinite program. Using a protein dimerization process as an example, we demonstrate that the proposed method can precisely predict the mean and the variance of the copy number of the monomer protein.
UR - http://www.scopus.com/inward/record.url?scp=85046131276&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046131276&partnerID=8YFLogxK
U2 - 10.1109/CDC.2017.8263820
DO - 10.1109/CDC.2017.8263820
M3 - Conference contribution
AN - SCOPUS:85046131276
T3 - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
SP - 1206
EP - 1211
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -